Quantitative measurements of relaxation parameters characterizing MRI signal, such as longitudinal (R1), transverse (R2 and R2*) and cross-relaxation rate constants have always been an important task in the field of quantitative MRI. Conventional inversion recovery (IR) and spin echo (SE) sequences have been used as the gold standard to measure R1 and R2, respectively. However, the long acquisition times have greatly hindered their clinical applications.
Compared to conventional IR techniques, variable flip angle (VFA) R1 mapping is based on imaging in steady state and can greatly reduce scan time, thus making 3D high-resolution R1 mapping of the whole brain feasible. Information on cross-relaxation parameters is usually obtained using off-resonance magnetization transfer (MT) saturation pulses. It was also realized that MT effects can greatly affect gradient echo signals, even in the absence of off-resonance MT saturation pulses thus causing systematic errors in VFA measurements.
Recently, a Gradient Echo Plural Contrast Imaging (GEPCI) technique based on a Gradient Recalled Echo (GRE) sequence with multiple gradient echoes has enabled simultaneous generation of naturally co-registered multi-contrast images (T1-weighted or spin density images, R2* maps and frequency maps) from a single MR scan. R2* mapping using GRE sequences has many advantages. First, the acquisition is fast, and therefore suffers from fewer motion artifacts. Second, due to the low flip angle used in GRE sequences, they have lower RF power deposition and are more suitable for high-field MRI. GRE sequences may also be sensitive to tissue-specific magnetic susceptibility effects, and hence may provide separate information on tissue cellular and hemodynamic properties.
Different methods have been used in the past to map the B1 field. The magnitude-based methods strongly rely on specific theoretical models that fail to account for complexity of biological tissues. As a result, previous methods are subject to biases and errors due to the limitations of the models imperfections and usually require a low-pass filter to eliminate noise in the data. To minimize this effect, long repetition times to suppress the T1 dependence of the signal can be used but demand very long acquisition times. The phase-based approaches are less dependent on a model than amplitude-based methods.